README.md

---
base_model: mistralai/Mistral-7B-Instruct-v0.3
library_name: peft
model_name: mistral7b_labels2codes_lora
tags:
- base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3
- lora
- sft
- transformers
- trl
licence: license
pipeline_tag: text-generation
---

# Model Card for mistral7b_labels2codes_lora

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3).
It has been trained using [TRL](https://github.com/huggingface/trl).

Mistral-7B Instruct — LoRA (ICD-10 labels → codes)

This LoRA adapter fine-tunes mistralai/Mistral-7B-Instruct-v0.3 to map French ICD-10 diagnostic labels (synonyms) to their corresponding codes (dot-less, up to 5 chars).


## Quick start

```python
from transformers import pipeline

question = "Libellé: Antécédent bronchite chronique"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```

## Training procedure

It was trained via supervised fine-tuning (QLoRA) on an instruction dataset built from (label, code) pairs (instruct_labels2codes) derived from a curated ICD-10 synonyms table created from the webscrapinng of aideaucodage.fr .

### Framework versions

- PEFT 0.17.0
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.2

## Citations



Cite TRL as:
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```